The AI in Business Podcast - Managing End Point Storage in Hybrid Data Strategies for Financial Services - with Yonas Yohannes of Oracle
The discussion highlights the transition from AI hype to practical applications, emphasizing the need for transparency and regulatory compliance in AI adoption. Jonas Johannes, CTO of FinServ and FIS at Oracle, explains the importance of endpoint storage in driving AI capabilities, especially in financial services. He stresses the need for AI solutions to be transparent, interpretable, and explainable to avoid regulatory risks. The conversation also covers the shift towards hybrid strategies in infrastructure, combining cloud and localized data centers to manage AI workloads effectively. Johannes points out the significance of data management and governance, advocating for banks to build their own LLMs to maintain control and reduce biases. He also discusses the potential of synthetic data in mitigating biases and enhancing AI training.
Key Points:
- AI's value lies in practical applications beyond hype, requiring transparency and regulatory compliance.
- Endpoint storage is crucial for AI capabilities, especially in financial services, enabling hybrid strategies.
- Banks should build their own LLMs to maintain control and reduce biases in AI applications.
- Data management and governance are critical for effective AI deployment, ensuring compliance and reducing risks.
- Synthetic data can help mitigate biases in AI training, enhancing model reliability.
Details:
1. 🎙️ Welcome and Introduction
1.1. Welcome
1.2. Overview of Podcast Topics
2. 🔍 Unpacking the AI Hype Cycle
2.1. AI Hype Cycle Overview
2.2. Challenges and Resource Requirements
2.3. Impact of Synthetic Data
2.4. Real Applications and Benefits
3. 🏦 AI's Role in Financial Services
3.1. Adoption and Experimentation
3.2. Use Cases and Long-term Benefits
3.3. Challenges and Key Elements for AI Solutions
4. 🛡️ Ensuring Transparency and Data Governance
4.1. Bias and Data Governance Issues
4.2. Generational Differences in AI
4.3. Hybrid Strategies in Cloud Technology
4.4. Infrastructure Requirements for AI
4.5. Data Management and Discipline
4.6. Shift Towards Cloud-Based Endpoint Solutions
4.7. Object Storage and Security
5. 🌐 Navigating Hybrid Strategies in AI
- Leveraging publicly trained LLMs like GPT-4 presents legal risks, including potential class action lawsuits due to proprietary content exposure.
- Financial institutions (FSIs) and FinTechs prioritize building their own LLMs to ensure data control and compliance.
- The emphasis is on controlling data rather than mere localization, ensuring compliance with regional regulations and governance.
- Utilizing public clouds is feasible if proper control mechanisms, including policy and governance structures, are in place.
- Ensuring that LLMs do not leave identifiable data footprints is crucial for maintaining data security and privacy.
- Case Study: A leading FSI developed a proprietary LLM that reduced compliance processing time by 40% while maintaining data integrity.
- Business Impact: Effective data management strategies have led to a 30% reduction in operational risks for FinTechs implementing their own LLMs.
6. 🤖 Addressing AI Bias with Synthetic Data
6.1. Data Sovereignty and Management
6.2. Leveraging Large Language Models (LLMs)
6.3. Role of Chief Data and Analytic Officers
6.4. Addressing Bias with Synthetic Data
7. 🔗 Key Takeaways and Conclusion
- Bias problems can easily infiltrate a Large Language Model (LLM) through the data sources used by large legacy organizations.
- Even if a diverse team handles the data, biased data inputs can lead to biased outputs.
- Bias is not limited to public data; private data can also carry biases that affect AI outputs.
- The issue of bias in AI systems is a significant concern that requires careful data management and selection.
- To mitigate bias, organizations should implement robust data auditing processes and use bias detection tools to ensure diverse and representative data sets.